Optimal scheduling method for electric vehicle energy storage charging and discharging based on V2G feasible region

Abstract

The present invention discloses an electric vehicle energy storage charging and discharging optimization scheduling method based on a V2G feasible domain, which specifically relates to the technical field of electric vehicle energy management. The method is used to solve the problem of scheduling failure caused by the instability of energy flow interaction when existing electric vehicles are performing regenerative braking energy recovery and V2G discharge simultaneously; by obtaining regenerative braking signals and remaining power data, it is determined based on a multi-source prediction algorithm whether the initial conditions of the feasible domain are met; when the conditions are met, the wheel axle braking distribution characteristics are analyzed through dynamic modeling and stochastic differential equations, and the real-time characteristic curve of vehicle discharge and grid adaptation is identified based on grid load data; the energy flow identification result of regenerative braking and V2G discharge is determined according to the characteristic curve and dynamic change characteristics; when the identification result is in a preset coupling interval, the feasible domain constraint conditions are calculated, the output power of the drive system and the bidirectional inverter are scheduled, and the regenerative braking recovery power and the grid discharge power are dynamically allocated.

Description

Technical Field

[0001] The present invention relates to the technical field of electric vehicle energy management, and more specifically, to a method for optimizing the charging and discharging of electric vehicle energy storage based on a V2G feasible region. Background Art

[0002] V2G technology is a key energy management method. It enables bidirectional energy flow between charging and discharging in electric vehicles, supporting peak-shaving and valley-filling for the power grid while also increasing the flexibility of electric vehicles' energy utilization. Furthermore, regenerative braking, a key feature of electric vehicles, converts mechanical energy generated during braking into electrical energy and stores it in the battery, thereby improving energy utilization. However, the parallel operation of V2G technology and regenerative braking requires complex dynamic operating conditions, requiring the coordinated management of multiple factors, including vehicle status, battery health, and grid load.

[0003] In the existing technology, when electric vehicles are performing regenerative braking energy recovery and V2G discharge simultaneously, scheduling failures are easily caused by the instability of energy flow interaction, making it difficult to effectively ensure the stability and efficiency of the charging and discharging process. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides an electric vehicle energy storage charging and discharging optimization scheduling method based on the V2G feasible region to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region includes the following steps:

[0007] Obtain the real-time braking signal of the regenerative braking system and the remaining power data of the battery management system, and analyze whether the initial determination conditions of the feasible domain are met based on the multi-source prediction algorithm;

[0008] When the initial conditions for the feasible region are met, the axle braking distribution characteristics are analyzed through vehicle dynamics modeling and stochastic differential equations to evaluate the dynamic changes in energy distribution during vehicle braking. The dynamic characteristics of the power grid are analyzed through grid load data modeling to identify the real-time characteristic curves of vehicle discharge and grid adaptation.

[0009] Based on the dynamic characteristics of energy distribution during vehicle braking and the real-time characteristic curve of vehicle discharge and grid adaptation, the energy flow identification results when the vehicle is undergoing regenerative braking and grid discharge in parallel are determined;

[0010] When the energy flow identification result is within the preset coupling range, the feasible region constraints that meet the vehicle driving safety and grid power requirements are determined;

[0011] Based on the constraints of the feasible region and the dynamic change characteristics of energy distribution during vehicle braking, the output power of the on-board drive system and bidirectional inverter is dispatched, and the recovery power generated by regenerative braking and the grid discharge power are dynamically allocated.

[0012] In a preferred embodiment, the real-time braking signal of the regenerative braking system and the remaining power data of the battery management system are obtained, and the analysis based on the multi-source prediction algorithm to determine whether the initial determination conditions of the feasible region are met specifically includes:

[0013] Acquiring real-time braking signals from the regenerative braking system, including collecting real-time data of brake pressure and wheel speed from brake pressure sensors and wheel speed sensors;

[0014] Obtaining the remaining power data of the battery management system, including obtaining the remaining power information and battery health status information of the vehicle battery through the battery management system;

[0015] A multi-source prediction algorithm is used to integrate and analyze real-time braking signals, remaining power information, and battery health status information to determine whether the initial conditions of the feasible domain for interaction between the vehicle and the power grid are met.

[0016] In a preferred embodiment, when the initial determination conditions of the feasible region are met, the axle braking distribution characteristics are analyzed through vehicle dynamics modeling and stochastic differential equations to evaluate the dynamic change characteristics of the energy distribution during vehicle braking, specifically including:

[0017] Based on the vehicle's real-time driving status information, wheel speed, brake pressure and vehicle posture data are collected. Vehicle posture data includes the vehicle's pitch angle and roll angle;

[0018] The wheel speed, brake pressure and vehicle posture data are calculated using the vehicle dynamics model to obtain the preliminary distribution of wheel axle braking energy;

[0019] A stochastic differential equation model is used to dynamically optimize the initial distribution of axle braking energy, eliminating the interference of road friction coefficient changes and braking unevenness on energy distribution.

[0020] The dynamic coefficient of brake energy distribution is calculated to quantify the dynamic change characteristics of energy distribution between axles during vehicle braking.

[0021] In a preferred embodiment, the dynamic coefficient of braking energy distribution is calculated to quantify the dynamic change characteristics of energy distribution between wheel axles during vehicle braking, specifically:

[0022] Calculate the dynamic coefficient of brake energy distribution:

[0023] Among them, C d Indicates the dynamic coefficient of braking energy distribution, F front Indicates the front wheel braking force, F rear Represents the rear wheel braking force, w f and w b are the weight factors of the front and rear wheels respectively, and ω f and ω b Both are greater than 0, and Δμ represents the difference in friction coefficient between the front and rear wheels.

[0024] In a preferred embodiment, when the initial determination conditions of the feasible region are met, the dynamic characteristics of the power grid are analyzed by power grid load data modeling to identify the real-time characteristic curve of vehicle discharge and power grid adaptation, specifically including:

[0025] Collect grid load data, including local grid voltage, frequency change rate, and load fluctuation characteristics;

[0026] Preprocess the collected grid load data, including normalization, noise filtering, and data alignment, to eliminate the interference of different sampling frequencies and noise on the analysis results;

[0027] A dynamic change model of the power grid load is constructed based on the time series model. The time series model uses historical data and real-time data of the power grid load to obtain the dynamic characteristics of the power grid load through fitting;

[0028] The grid load dynamic change model and vehicle discharge parameters are used to analyze the real-time adaptability of the grid load to vehicle discharge, and the real-time characteristic curve of vehicle discharge and grid adaptation is output.

[0029] In a preferred embodiment, a power grid load dynamic change model is constructed based on a time series model. The time series model uses historical data and real-time data of the power grid load to obtain the dynamic characteristics of the power grid load by fitting, specifically:

[0030] The autoregressive integral moving average model is used to fit the grid load data. The formula is:

[0031] Y t =φ1Y t-1 +φ2Y t-2 +…+φ p Y t-p +∈ t +θ1∈ t-1 +…+θ q ∈ t-q ;

[0032] Among them, Y tIndicates the grid load value at time t, φ1, φ2, ..., φ p represents the autoregressive coefficient, ∈ t represents the error term at time t, θ1, θ2, …, θ q represents the moving average coefficient, p and q represent the autoregressive order and the moving average order respectively;

[0033] The time series model uses historical data to predict the dynamic changes of load and provide the dynamic characteristics of power grid load.

[0034] In a preferred embodiment, the grid load dynamic change model and vehicle discharge parameters are used to analyze the real-time adaptability of the grid load to vehicle discharge, and a real-time characteristic curve of vehicle discharge and grid adaptation is output, specifically:

[0035] Conduct adaptability analysis on the dynamic change data of grid load generated by time series model and vehicle discharge parameters;

[0036] Analyze the real-time adaptability of the grid load to vehicle discharge and calculate the adaptability index: Among them, S represents the adaptability index, P i Represents the difference between the vehicle discharge power and the grid load power at the i-th sampling point, W i represents the load priority weight of the i-th sampling point, and n represents the total number of load data sampling points in the sampling period;

[0037] The adaptability index generated by the adaptability analysis generates a real-time characteristic curve of vehicle discharge and grid adaptation.

[0038] In a preferred embodiment, based on the dynamic change characteristics of energy distribution during vehicle braking and the real-time characteristic curve of vehicle discharge and grid adaptation, the energy flow identification result when the vehicle is performing regenerative braking and grid discharge in parallel is determined, specifically including:

[0039] The dynamic coefficient of brake energy distribution and the adaptability index are input into the energy flow identification model. The energy flow identification model is based on the threshold judgment rule and is calculated according to the following rules:

[0040] When the dynamic coefficient of the braking energy distribution is greater than the corresponding preset threshold and the adaptability index is greater than the corresponding preset threshold, the energy flow identification result is a coordinated state;

[0041] When the dynamic coefficient of braking energy distribution is greater than the corresponding preset threshold but the adaptability index is less than or equal to the corresponding preset threshold, the energy flow identification result is regenerative braking priority;

[0042] When the dynamic coefficient of braking energy distribution is less than or equal to the corresponding preset threshold and the adaptability index is greater than the corresponding preset threshold, the energy flow identification result is grid discharge priority;

[0043] When the dynamic coefficient of the braking energy distribution and the adaptability index are both less than or equal to the corresponding preset thresholds, the energy flow identification result is a non-cooperative state;

[0044] The energy flow identification result is output according to the calculation result of the energy flow identification model. The energy flow identification result is used to characterize the coordinated state of the regenerative braking energy and the grid discharge power under the current working condition.

[0045] In a preferred embodiment, when the energy flow identification result is within the preset coupling interval, determining the feasible region constraints that meet vehicle driving safety and grid power requirements specifically includes:

[0046] Determining whether the energy flow identification result is within a preset coupling interval, where the preset coupling interval is used to characterize the coordinated state range of the regenerative braking energy and the grid discharge power;

[0047] When the energy flow identification result is within the preset coupling interval, the vehicle driving state parameters are obtained, including vehicle speed, vehicle attitude angle and wheel slip rate, which are used to describe the dynamic safety state of the vehicle;

[0048] Obtain grid power status parameters, including real-time grid voltage, frequency change rate, and load fluctuation characteristics, to describe the dynamic characteristics of grid power demand;

[0049] Based on the vehicle driving state parameters and the grid power state parameters, the feasible region constraints are calculated through a multi-objective optimization algorithm. The multi-objective optimization algorithm uses the vehicle driving safety threshold and the grid power demand threshold as constraints to dynamically adjust the energy flow distribution ratio.

[0050] Output feasible region constraints to limit the distribution range of vehicle regenerative braking energy recovery and grid discharge power.

[0051] In a preferred embodiment, based on the feasible region constraints and the dynamic change characteristics of energy distribution during vehicle braking, the output power of the vehicle drive system and the bidirectional inverter is dispatched, and the recovery power generated by regenerative braking and the grid discharge power are dynamically allocated, specifically including:

[0052] According to the feasible region constraints and the dynamic coefficient of braking energy distribution, the maximum allocation ratio of the recovery power generated by regenerative braking and the maximum allocation ratio of the grid discharge power are calculated;

[0053] Based on the calculation results, the regenerative braking recovery power provided by the vehicle drive system and the grid discharge power output by the bidirectional inverter are determined respectively;

[0054] The calculation results of regenerative braking recovery power and grid discharge power are transmitted as dynamic allocation instructions to the vehicle drive system and bidirectional inverter to adjust their actual output power and ensure that power is dynamically distributed in proportion.

[0055] The technical effects and advantages of the electric vehicle energy storage charging and discharging optimization scheduling method based on the V2G feasible region of the present invention are as follows:

[0056] 1. By introducing initial conditions for determining the feasible region, the coordinated scheduling of electric vehicle regenerative braking energy recovery and V2G discharge operations is confined to a reasonable range that meets vehicle driving safety and grid power requirements, effectively avoiding scheduling failures caused by energy flow interference. Vehicle dynamics modeling and stochastic differential equations are used to analyze the dynamic changes in energy distribution during vehicle braking. Simultaneously, grid load data-based modeling identifies the real-time characteristic curves of vehicle discharge and grid adaptation, enabling precise identification and dynamic management of the coordinated energy flow state. This method can adapt to complex dynamic operating conditions and improve the stability and efficiency of charge and discharge scheduling.

[0057] 2. Through a power allocation optimization algorithm based on feasible region constraints, the output power of the vehicle drive system and bidirectional inverter is dynamically scheduled, ensuring that the recovered power generated by regenerative braking and the power discharged to the grid are distributed proportionally to meet the actual needs of the vehicle and the grid. This dynamic allocation strategy flexibly adjusts the energy distribution scheme between regenerative braking and grid discharge based on energy flow identification results and real-time operating conditions. This not only ensures vehicle driving safety, but also further improves energy utilization efficiency and reduces the risk of grid load fluctuations, providing technical support for the efficient collaboration between electric vehicles and the grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 Schematic diagram of the electric vehicle energy storage charging and discharging optimization scheduling method based on the V2G feasible region of the present invention. DETAILED DESCRIPTION

[0059] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] Example: Figure 1 The present invention provides an electric vehicle energy storage charging and discharging optimization scheduling method based on the V2G feasible region, which includes the following steps:

[0061] The real-time braking signal of the regenerative braking system and the remaining power data of the battery management system are obtained, and the initial determination conditions of the feasible domain are analyzed based on the multi-source prediction algorithm.

[0062] When the initial conditions for the feasible domain are met, the axle braking distribution characteristics are analyzed through vehicle dynamics modeling and stochastic differential equations to evaluate the dynamic change characteristics of energy distribution during vehicle braking. The dynamic characteristics of the power grid are analyzed through grid load data modeling to identify the real-time characteristic curves of vehicle discharge and grid adaptation.

[0063] Based on the dynamic change characteristics of energy distribution during vehicle braking and the real-time characteristic curve of vehicle discharge and grid adaptation, the energy flow identification results when the vehicle performs regenerative braking and grid discharge in parallel are determined.

[0064] When the energy flow identification result is in the preset coupling interval, the feasible domain constraints that meet the vehicle driving safety and grid power requirements are determined.

[0065] Based on the constraints of the feasible region and the dynamic change characteristics of energy distribution during vehicle braking, the output power of the on-board drive system and bidirectional inverter is dispatched, and the recovery power generated by regenerative braking and the grid discharge power are dynamically allocated.

[0066] Obtain the real-time braking signal of the regenerative braking system and the remaining power data of the battery management system, and analyze whether the initial determination conditions of the feasible domain are met based on the multi-source prediction algorithm, including:

[0067] Acquire real-time braking signals from the regenerative braking system, including collecting real-time data of brake pressure and wheel speed from the brake pressure sensor and wheel speed sensor:

[0068] Real-time data of brake pressure and wheel speed are collected from the vehicle's regenerative braking system. The brake pressure sensor installed in the vehicle's braking system monitors the pressure changes of the braking system in real time, and the collected pressure signal is converted into an electrical signal. The wheel speed sensor installed at the wheel obtains the vehicle's wheel speed data in real time and saves the speed data in the form of a digital signal.

[0069] Real-time data of brake pressure and wheel speed are key parameters for judging the vehicle's current braking status and energy recovery.

[0070] Obtain the remaining power data of the battery management system, including the remaining power information and battery health status information of the vehicle battery through the battery management system:

[0071] The battery management system monitors the battery's operating status in real time through the voltage, current and temperature sensors inside the battery and calculates the battery's remaining power value. The battery management system calculates and evaluates the battery's health status information, including the battery's remaining service life parameters and battery capacity decay rate, as the basis for battery status evaluation.

[0072] A multi-source prediction algorithm is used to integrate and analyze real-time braking signals, remaining power information, and battery health status information to determine whether the initial conditions for the feasible domain of interaction between the vehicle and the power grid are met:

[0073] The collected brake pressure data, wheel speed data, and the remaining power information and battery health status information output by the battery management system are normalized to eliminate differences between different data types and units.

[0074] Feature extraction based on multi-source prediction algorithm extracts key feature parameters from normalized data, such as brake pressure peak, wheel speed change rate, remaining power percentage and battery health status level, to form a comprehensive feature vector.

[0075] Using the decision model of the multi-source prediction algorithm, the feature vector is input into the judgment module, and the initial judgment result of the feasible domain of interaction between the vehicle and the power grid is obtained through calculation, specifically including whether the necessary conditions for energy flow interaction are met.

[0076] The necessary conditions for energy flow interaction refer to the basic requirements that electric vehicles must meet when interacting with the power grid (such as regenerative braking energy recovery and discharging to the grid), including vehicle status requirements, grid status requirements, and communication and control requirements.

[0077] Vehicle status requirements include:

[0078] Regenerative braking capability: The vehicle should have effective regenerative braking function, which can convert mechanical energy into electrical energy and recover it during braking;

[0079] Remaining battery capacity: The remaining battery capacity should be above the set minimum threshold to ensure that the vehicle can still meet its energy needs for normal driving after discharging to the grid.

[0080] Battery health: The battery should be in good health, with sufficient capacity and performance to support frequent charge and discharge operations.

[0081] Grid status requirements include:

[0082] Grid demand: The grid should have a current need to accept vehicle discharges, such as requiring additional power during peak hours.

[0083] Grid parameters: Grid parameters such as voltage and frequency should be within the permitted range to ensure the safety and stability of energy exchange.

[0084] Communication and control requirements include:

[0085] Information exchange: There should be a reliable communication channel between the vehicle and the power grid, enabling real-time exchange of status information and control instructions;

[0086] Control strategy: There should be a clear control strategy to ensure coordination and optimization during the energy interaction process.

[0087] Multi-source prediction algorithms combine data from multiple sources to predict vehicle energy demand and allocation. These data sources may include vehicle speed, acceleration, road grade, traffic conditions, driver behavior, and battery status. By integrating and analyzing this multi-dimensional data, the algorithm can more accurately predict vehicle energy demand, thereby optimizing energy management strategies and improving vehicle energy efficiency and battery life.

[0088] The steps to implement the multi-source prediction algorithm are as follows:

[0089] Data collection: Collect relevant data from various sensors and systems in the vehicle, such as brake pressure, wheel speed, remaining battery power and health status.

[0090] Data preprocessing: Clean, normalize, and extract features from the collected data to eliminate differences between different data types and units and ensure data quality and consistency.

[0091] Feature fusion: Fusing pre-processed multi-source data to form a comprehensive feature vector. This can be achieved through methods such as concatenation, weighted averaging, or deep learning models.

[0092] Model training: Using historical data, machine learning or deep learning algorithms (such as support vector machines, random forests, neural networks, etc.) are used to train the prediction model so that it can learn the relationship between vehicle energy demand and multi-source characteristics.

[0093] Real-time prediction: In practical applications, multi-source data collected in real time is input into the trained model to predict the vehicle's energy demand or allocation strategy.

[0094] Decision-making and control (i.e., decision-making model): Based on the prediction results, the vehicle's energy management strategy is adjusted, such as the allocation of regenerative braking energy recovery and grid discharge power, to optimize vehicle performance and energy utilization efficiency.

[0095] Based on the results of the multi-source prediction algorithm, it is determined whether the vehicle currently meets the basic requirements for interacting with the power grid. The judgment basis includes:

[0096] Real-time braking status: whether there is sufficient regenerative braking energy recovery capability; battery status: whether there is sufficient remaining power and health status to support grid discharge needs.

[0097] If the above conditions are met (sufficient regenerative braking energy recovery capability and sufficient remaining power and health status to support the grid discharge demand), it is marked as feasible, that is, the initial judgment conditions of the feasible domain are met. Otherwise, it enters the standby state or adjusts the strategy and re-judges.

[0098] The basic requirements refer to the minimum conditions that need to be met when vehicles interact with the power grid, including:

[0099] Regenerative braking capability: The vehicle should be able to effectively recover braking energy and convert it into electrical energy;

[0100] Battery status: The battery should have sufficient remaining charge and health status to support the demand for discharging to the grid.

[0101] Sufficient remaining capacity means the battery's remaining capacity should be above a set minimum threshold to ensure that the vehicle's energy needs can still be met after discharging to the grid. This threshold can be determined based on factors such as vehicle type, usage, and battery capacity.

[0102] Sufficient regenerative braking energy recovery capability refers to the vehicle's regenerative braking system's ability to recover sufficient energy during braking and effectively store it in the battery. This capability can be assessed through the regenerative braking system's design parameters and actual performance indicators.

[0103] When the initial conditions for the feasible region are met, the axle braking distribution characteristics are analyzed through vehicle dynamics modeling and stochastic differential equations to evaluate the dynamic change characteristics of energy distribution during vehicle braking, including:

[0104] Based on the real-time driving status information of the vehicle, wheel speed, brake pressure and vehicle posture data are collected. The vehicle posture data includes the pitch angle and roll angle of the vehicle.

[0105] The wheel speed is collected in real time by a speed sensor installed on the wheel axle. The speed data reflects the movement state of the vehicle during braking.

[0106] Brake pressure is collected by a brake pressure sensor installed in the hydraulic line of the brake system. Brake pressure data is used to describe the braking force applied during vehicle braking.

[0107] Vehicle attitude data includes the vehicle's pitch and roll angles, acquired via an attitude sensor mounted at the center of the vehicle chassis. The pitch angle reflects the vehicle's longitudinal tilt, while the roll angle reflects its lateral tilt.

[0108] The vehicle dynamics model is used to calculate the wheel speed, brake pressure, and vehicle posture data to obtain the preliminary distribution of axle braking energy:

[0109] The vehicle dynamics model describes the mechanical behavior of a vehicle during braking, involving the relationship between wheel speed, brake pressure, and vehicle posture data. Parameters such as the vehicle's center of gravity, wheelbase, and wheel friction characteristics are incorporated into the dynamics model.

[0110] According to the vehicle dynamics model, the braking force distribution acting on the front and rear wheels during vehicle braking is calculated. The specific calculation formula is as follows: Among them, F front Indicates the front wheel braking force; F rear Indicates rear wheel braking force; P b Indicates brake pressure, collected by brake pressure sensor; A f and A r are the front wheel braking area and the rear wheel braking area respectively; μ f and μ r are the friction coefficients of the front and rear wheels respectively; θ pitch represents the vehicle's pitch angle, which is collected by the attitude sensor; L represents the vehicle's wheelbase, which is a geometric parameter of the vehicle.

[0111] Through the above calculations, the preliminary distribution of the wheel axle braking force is obtained, and this distribution data will be used in the subsequent optimization analysis steps.

[0112] The stochastic differential equation model is used to dynamically optimize the initial distribution of axle braking energy, eliminating the interference of road friction coefficient changes and braking unevenness on energy distribution:

[0113] To account for the effects of road friction coefficient variations and braking nonuniformity on energy distribution, a stochastic differential equation (SDE) is introduced to dynamically optimize the axle braking energy distribution. The SDE is as follows: dF = α·F·dt + β·σ·dW. dF represents the dynamic variation of the braking force, describing its temporal dynamics. α represents the time constant for braking force decay, expressed in reciprocal seconds and determined by the braking system's response characteristics. F represents the current braking force value, derived from the calculation results of the dynamic model. dt represents the time increment, simulating the continuous variation of the braking force. β represents the sensitivity factor for road friction coefficient variations, measuring the sensitivity of the braking force to road friction coefficient variations. Higher values ​​indicate a more pronounced response. σ represents the random perturbation intensity, describing the influence of external random factors (such as road surface unevenness or sensor noise) on the braking force variation. Higher values ​​indicate a more pronounced effect of random perturbations on the system. dW represents the Brownian motion term, describing the random perturbation, which follows a standard normal distribution and models the unpredictable random factors in the braking force variation.

[0114] The axle braking force distribution value obtained by preliminary calculation is substituted into the stochastic differential equation. Combined with the actual road friction coefficient (collected by road surface sensors) and the random disturbance model, the braking force is iteratively optimized to eliminate the influence of dynamic interference on the braking force distribution.

[0115] The dynamic coefficient of brake energy distribution is calculated to quantify the dynamic characteristics of energy distribution between axles during vehicle braking:

[0116] Finally, the dynamic coefficient of braking energy distribution is calculated to quantify the dynamic change characteristics of energy distribution between axles. The calculation formula is: Among them, C d Indicates the dynamic coefficient of braking energy distribution; w f and w b are the weight factors of the front and rear wheels, respectively, reflecting the influence of the vehicle's center of gravity on the distribution of braking forces on the front and rear wheels, and w f and w b Both are greater than 0; Δμ represents the difference in friction coefficient between the front and rear wheels, which is used to correct the impact of uneven road conditions on energy distribution.

[0117] For front-wheel drive vehicles, the weight of the front wheels can be increased (for example, by increasing the front-wheel priority allocation ratio). For rear-wheel drive vehicles, the weight of the rear wheels can be appropriately increased to make the model more suitable for actual working conditions.

[0118] The dynamic braking energy distribution coefficient (BED) reflects the proportional relationship between the front and rear wheels' braking energy distribution. This coefficient serves as a quantitative indicator of the vehicle's dynamic braking energy distribution characteristics. A larger BED coefficient indicates a greater bias toward the front wheels during braking, with relatively less involvement from the rear wheels. In front-wheel drive vehicles, this is generally a normal phenomenon consistent with vehicle design characteristics. In rear-wheel drive or four-wheel drive vehicles, an excessively large BED coefficient may indicate uneven braking force distribution and requires evaluation based on the specific drive mode and operating conditions.

[0119] When the initial conditions for the feasible region are met, the grid dynamic characteristics are analyzed through grid load data modeling to identify the real-time characteristic curve of vehicle discharge and grid adaptation, including:

[0120] Collect grid load data, including local grid voltage, frequency change rate, and load fluctuation characteristics:

[0121] Monitoring equipment installed on the grid side collects voltage data from local areas of the grid. The equipment records the voltage value during each sampling cycle. This local grid voltage data is used to reflect the impact of load changes on grid power supply stability.

[0122] Real-time detection of grid frequency fluctuations and calculation of the frequency change rate as a key parameter of load dynamic characteristics. The frequency change rate reflects the degree of interference of load fluctuations on grid frequency stability and is an important data point for dynamic analysis.

[0123] Load monitoring devices collect load fluctuation characteristic data, including real-time load power changes, load on / off status, and other information. This data is used to describe the dynamic behavior of the grid load and directly affects the compatibility of the vehicle and the grid.

[0124] The collected grid load data is preprocessed, including normalization, noise filtering, and data alignment, to eliminate the interference of different sampling frequencies and noise on the analysis results:

[0125] The collected grid load data has different dimensions due to different data sources, so it needs to be normalized to bring it to the same magnitude. Normalization ensures that the local voltage, frequency change rate, and load fluctuation characteristics of the grid are weighted equally, facilitating subsequent analysis.

[0126] Grid load data is subjected to noise filtering. This process converts the data from the time domain to the frequency domain through Fourier transform, removing high-frequency noise. Noise filtering eliminates the effects of sensor errors and environmental interference on the data, improving the accuracy of analysis results.

[0127] The normalized voltage data, frequency change rate, and load fluctuation characteristic data are aligned on a unified time axis to ensure consistent timestamps for subsequent modeling and analysis.

[0128] A dynamic change model of the power grid load is constructed based on the time series model. The time series model uses historical data and real-time data of the power grid load to obtain the dynamic characteristics of the power grid load through fitting:

[0129] The grid load dynamic change model combines historical data and real-time data. Historical data is obtained through long-term grid monitoring records, while real-time data comes from currently collected data.

[0130] The autoregressive integrated moving average model (ARI MA model) is used to fit the power grid load data. The formula is:

[0131] Y t =φ1Y t-1 +φ2Y t-2 +…+φ p Y t-p +∈ t +θ1∈ t-1 +…+θ q ∈ t-q ;

[0132] Among them, Y t Indicates the grid load value at time t; φ1, φ2, ..., φ p represents the autoregressive coefficient; ∈ t represents the error term at time t; θ1, θ2, …, θ q represents the moving average coefficient; p and q represent the autoregressive order and the moving average order, respectively.

[0133] The time series model uses historical data to predict the dynamic changes of load and provide the dynamic characteristics of grid load for subsequent analysis.

[0134] The grid load dynamic change model and vehicle discharge parameters are used to analyze the real-time adaptability of the grid load to vehicle discharge, and the real-time characteristic curve of vehicle discharge and grid adaptation is output:

[0135] The dynamic change data of the grid load generated by the time series model is used to conduct adaptability analysis with the vehicle discharge parameters (including the vehicle's output power, discharge voltage and load demand).

[0136] Analyze the real-time adaptability of the grid load to vehicle discharge and calculate the adaptability index: Among them, S represents the adaptability index, which is used to quantify the matching degree between the vehicle discharge power and the grid load demand. The larger the value, the more suitable the vehicle discharge power is for the current grid load demand, and vice versa. P i W represents the difference between the vehicle discharge power and the grid load power at the i-th sampling point; irepresents the load priority weight of the i-th sampling point, reflecting the importance of different loads to grid stability or vehicle discharge requirements; n represents the total number of load data sampling points in the sampling period.

[0137] The adaptability analysis results generate a real-time characteristic curve of vehicle discharge and grid adaptation. The curve describes the degree of adaptation of the vehicle discharge power under different grid load conditions for subsequent optimization and scheduling.

[0138] Based on the dynamic characteristics of energy distribution during vehicle braking and the real-time characteristic curve of vehicle discharge and grid adaptation, the energy flow identification results when the vehicle is undergoing regenerative braking and grid discharge in parallel are determined, specifically including:

[0139] The dynamic coefficient of brake energy distribution and the adaptability index are input into the energy flow identification model. The energy flow identification model is based on the threshold judgment rule and is calculated according to the following rules:

[0140] The dynamic coefficient of the brake energy distribution and the adaptability index are input into the energy flow identification model. The dynamic coefficient of the brake energy distribution and the adaptability index are calculated and normalized according to the above steps to ensure that the parameters are within a uniform range for collaborative calculation.

[0141] When the dynamic coefficient of the braking energy distribution is greater than the corresponding preset threshold and the adaptability index is greater than the corresponding preset threshold, the energy flow identification result is "cooperative state"; priority is given to matching energy flow parallel operation: in this case, the vehicle's front wheel braking force is dominant, and the vehicle's discharge power is highly adapted to grid demand, indicating that both regenerative braking and discharge operations are in good condition. Therefore, priority matching energy flow parallel operation is given. Prioritizing parallel operation fully utilizes regenerative braking energy and supports the grid.

[0142] When the dynamic coefficient of braking energy distribution exceeds the corresponding preset threshold but the adaptability index is less than or equal to the corresponding preset threshold, the energy flow identification result is "regenerative braking priority"; regenerative braking energy recovery is prioritized: if regenerative braking energy is dominant toward the front wheels, but the grid discharge power is poorly matched with the load demand, priority should be given to regenerative braking energy recovery to reduce resource waste from grid discharge. This avoids inefficient energy flow matching and meets the technical application scenario.

[0143] When the dynamic coefficient of braking energy distribution is less than or equal to the corresponding preset threshold and the adaptability index is greater than the corresponding preset threshold, the energy flow identification result is "grid discharge priority." Grid discharge operation is prioritized: when the front and rear wheel braking force distribution is balanced, the regenerative braking energy distribution is relatively stable, and the vehicle discharge power is highly aligned with grid demand, grid discharge operation is prioritized. This ensures that grid load demand is met first, meeting the goal of stable grid operation.

[0144] When both the dynamic coefficient of braking energy distribution and the adaptability index are less than or equal to the corresponding preset thresholds, the energy flow identification result is "non-coordinated state"; the priority of energy flow parallel operation is reduced: If the vehicle's regenerative braking energy distribution is balanced, but the discharge is not well matched, it means that the conditions for energy flow coordination are not met under the current operating conditions. In this case, the priority of energy flow parallel operation should be reduced. Avoid forcing energy flow operations in non-coordinated states to reduce ineffective energy distribution.

[0145] When the dynamic braking energy distribution coefficient is greater than its corresponding preset threshold, the vehicle's energy distribution tends to be dominated by front-wheel braking force. In this case, regenerative braking energy may be preferentially distributed to the front wheels, resulting in insufficient energy recovery at the rear wheels. When the dynamic braking energy distribution coefficient is less than or equal to the preset threshold, the vehicle's front and rear wheels have a more balanced braking force distribution, resulting in more stable regenerative braking energy recovery.

[0146] When the adaptability index is greater than its corresponding preset threshold, it indicates that the vehicle's discharge power can well meet the grid load demand. In this case, vehicle discharge is prioritized to support grid stability. When the adaptability index is less than or equal to the preset threshold, it indicates that the vehicle's discharge power is less compatible with the grid load demand. In this case, discharge priority is reduced and the power allocation strategy may need to be adjusted.

[0147] The energy flow identification results are output based on the calculation results of the energy flow identification model. The energy flow identification results are used to characterize the coordinated state of regenerative braking energy and grid discharge power under the current working conditions:

[0148] Based on the judgment rules of the energy flow identification model, the energy flow identification results are output. The identification results include: the priority status of the current regenerative braking energy distribution; the adaptability status of the grid discharge power; and the coordination priority of regenerative braking and grid discharge.

[0149] Energy flow identification model judgment rules: Based on the output of the energy flow identification model, a series of thresholds and conditions are set to determine whether the current energy flow state meets the preset operating mode or safety requirements. For example, when the vehicle's regenerative braking power exceeds a certain set value and the grid load demand reaches a specific level, the system determines that V2G discharge operation is suitable.

[0150] Energy flow identification results: Based on the aforementioned model and judgment rules, the output indicates the current energy flow status, such as "regenerative braking energy recovery only," "V2G discharge only," "regenerative braking and V2G discharge in parallel," or "no energy flow." This result serves as the basis for subsequent energy management and scheduling strategies.

[0151] The energy flow identification model is used to analyze and determine the state and characteristics of energy flow during the vehicle-to-grid (V2G) process of recovering energy through regenerative braking. By collecting real-time vehicle operating data (such as speed, acceleration, and braking signals) and grid status data (such as voltage, frequency, and load), data-driven algorithms (such as deep learning models) are used to identify and classify energy flow patterns, thereby determining the type and characteristics of the current energy flow.

[0152] The output recognition results are used to characterize the coordinated state of the vehicle's regenerative braking energy and the grid's discharge power under the current operating conditions, such as: the proportional distribution of energy recovery and discharge; the dynamic adjustment of the regenerative braking energy priority; and whether the current power scheduling strategy needs to be changed.

[0153] It's worth noting that the preset threshold for the dynamic coefficient of brake energy distribution is used to determine whether the front and rear wheel braking force distribution is appropriate. This threshold is set based on the vehicle type (front-wheel drive, rear-wheel drive, or four-wheel drive) and driving conditions (such as road friction coefficient and braking demand), and is typically determined through vehicle dynamics testing. The preset threshold for the adaptability index quantifies the compatibility of the vehicle's discharge power with the grid load requirements. This threshold is set based on the grid's dynamic load characteristics, voltage stability, and the vehicle's output power capability, and is adjusted based on real-time analysis of grid load fluctuations and vehicle discharge capacity.

[0154] When the energy flow identification result is within the preset coupling range, the feasible region constraints that meet vehicle driving safety and grid power requirements are determined, including:

[0155] Determine whether the energy flow identification result is within the preset coupling interval. The preset coupling interval is used to characterize the coordinated state range of regenerative braking energy and grid discharge power:

[0156] The preset coupling interval represents an effective range for the coordinated operation of regenerative braking energy and grid discharge power.

[0157] The specific definitions are as follows:

[0158] Collaborative interval: The corresponding energy flow identification result is "collaborative state"; non-collaborative interval: The corresponding energy flow identification result is "regenerative braking priority", "grid discharge priority" or "non-collaborative state".

[0159] It is determined whether the energy flow identification result is in the cooperative interval. If yes, the energy flow identification result is in the preset coupling interval.

[0160] When the energy flow identification result is within the preset coupling range, the vehicle driving state parameters are obtained, including vehicle speed, vehicle attitude angle, and wheel slip rate, which are used to describe the dynamic safety state of the vehicle:

[0161] Vehicle speed: obtained through the vehicle speed sensor and used to represent the real-time running speed of the vehicle.

[0162] Vehicle attitude angle: obtained through the on-board inertial measurement unit, specifically including pitch angle and roll angle, used to describe the dynamic attitude of the vehicle.

[0163] Wheel slip rate: Calculated by vehicle speed and wheel speed, the formula is as follows: Among them, S r Indicates the wheel slip rate, V t Indicates the vehicle speed, V w Indicates the wheel speed.

[0164] The above parameters are collected by vehicle sensors and stored in the vehicle controller as input for subsequent calculation of feasible domain constraints.

[0165] Obtain grid power status parameters, including real-time grid voltage, frequency change rate, and load fluctuation characteristics, to describe the dynamic characteristics of grid power demand:

[0166] The real-time voltage is collected by the grid monitoring equipment; the frequency change rate is calculated by the frequency sensor; the load fluctuation characteristics, including the load change rate and the load on state, are collected by the load monitoring equipment.

[0167] The above parameters are collected by the grid-side monitoring equipment and transmitted to the vehicle controller through the communication module for subsequent optimization calculations.

[0168] Based on the vehicle driving state parameters and the grid power state parameters, the feasible region constraints are calculated through a multi-objective optimization algorithm. The multi-objective optimization algorithm uses the vehicle driving safety threshold and the grid power demand threshold as constraints to dynamically adjust the energy flow distribution ratio:

[0169] The multi-objective optimization algorithm takes the following data as input: vehicle driving state parameters, including vehicle speed, vehicle attitude angle and wheel slip rate; grid power state parameters, including real-time voltage, frequency change rate and load fluctuation characteristics.

[0170] The goal of the multi-objective optimization algorithm is to meet the vehicle driving safety threshold and the grid power demand threshold, and dynamically adjust the distribution ratio of regenerative braking energy and grid discharge power. The optimization objective can be expressed as: Among them, W v is the weight of the vehicle driving safety parameter, which indicates the importance of the vehicle driving state in the optimization objective and is used to balance the priority of vehicle driving safety and grid power demand; S r is the wheel slip rate, which indicates the slip rate of the wheel under the current working condition and reflects the wheel grip and the dynamic safety status of the vehicle; S this the vehicle driving safety threshold, which indicates the safety critical value of the vehicle slip rate and is used to determine whether the vehicle dynamic stability meets the safety requirements; W g is the weight of the grid power parameter, which indicates the importance of the grid power demand in the optimization objective and is used to balance the relationship between the grid load demand and vehicle driving safety; P d P is the real-time power demand of the power grid, which indicates the current power demand on the power grid side and is collected in real time by the power grid load monitoring equipment; g The vehicle discharge power indicates the actual discharge power released by the vehicle to the grid, which is regulated and output by the vehicle controller in real time.

[0171] By optimizing the calculation results, the distribution ratio of regenerative braking energy and grid discharge power is dynamically adjusted to ensure that the constraints of driving safety and power demand are met.

[0172] Output feasible region constraints to limit the distribution range of vehicle regenerative braking energy recovery and grid discharge power:

[0173] The feasible region constraints include the maximum allocation ratio of regenerative braking energy recovery and the maximum allocation ratio of grid discharge power. The optimized feasible region constraints are transmitted to the vehicle controller as control instructions.

[0174] Based on the constraints of the feasible region and the dynamic characteristics of energy distribution during vehicle braking, the output power of the onboard drive system and bidirectional inverter is dispatched, and the recovery power generated by regenerative braking and the power discharged from the grid are dynamically allocated. Specifically, the following are performed:

[0175] According to the feasible region constraints and the dynamic coefficient of braking energy distribution, the maximum distribution ratio of the recovery power generated by regenerative braking and the maximum distribution ratio of the grid discharge power are calculated:

[0176] The feasible region constraints, derived from the preceding steps, are used to define the range of the vehicle's distribution between regenerative braking energy recovery and grid discharge. The dynamic braking energy distribution coefficient quantifies the dynamic distribution ratio of front and rear wheel braking force during vehicle braking.

[0177] According to the constraints of the feasible region and the dynamic coefficient of braking energy distribution, the distribution ratio of the recovery power generated by regenerative braking and the discharge power of the grid is determined.

[0178] The allocation ratio of the regenerative braking power is the ratio of the feasible region constraint value to the sum of the feasible region constraint value and the dynamic coefficient of the braking energy distribution. The allocation ratio of the grid discharge power is the value of one minus the allocation ratio of the regenerative braking power.

[0179] Through the above calculation method, the maximum distribution ratio of regenerative braking recovery power and grid discharge power is obtained to ensure that the distribution ratio meets the energy flow requirements.

[0180] Based on the calculation results, the regenerative braking recovery power provided by the vehicle drive system and the grid discharge power output by the bidirectional inverter are determined respectively:

[0181] The vehicle's maximum power output represents the maximum output power that the vehicle can provide in its current state, and is determined in real time by the operating conditions of the vehicle's powertrain.

[0182] The recovery power generated by regenerative braking and the grid discharge power are calculated based on the allocation ratio: the recovery power generated by regenerative braking is the allocation ratio of the recovery power generated by regenerative braking multiplied by the maximum power output that the vehicle can provide; the grid discharge power is the allocation ratio of the grid discharge power multiplied by the maximum power output that the vehicle can provide.

[0183] The calculation results of regenerative braking recovery power and grid discharge power are transmitted as dynamic distribution instructions to the vehicle drive system and bidirectional inverter to adjust their actual output power and ensure that power is dynamically distributed proportionally:

[0184] Based on the calculation results, dynamic allocation instructions are generated, including: the power setting value that the regenerative braking system needs to recover; the power setting value that the bidirectional inverter needs to output to the power grid.

[0185] The dynamic distribution instructions are transmitted to the regenerative braking system and the bidirectional inverter respectively through the vehicle's internal communication network.

[0186] The regenerative braking system dynamically adjusts the actual recovered power based on the instructions received; the bidirectional inverter dynamically adjusts the power output to the grid based on the instructions, ensuring that the vehicle's regenerative braking recovery power and the grid's discharge power are dynamically distributed according to the distribution ratio.

[0187] During the dynamic allocation process, the actual power output of the regenerative braking system and the bidirectional inverter can be monitored in real time. If the deviation between the actual output power and the allocation instruction target value exceeds the allowable range, the allocation instruction is adjusted in real time to ensure that the actual output is consistent with the allocation ratio.

[0188] It is worth noting that the V2G feasible domain can be understood as all possible states or operating ranges of electric vehicles participating in V2G operations (such as charging and discharging) under the conditions of meeting the needs of the electric vehicles themselves and the constraints of the power grid.

[0189] To better understand the V2G feasible domain, in V2G technology, electric vehicles not only act as electricity consumers, but can also feed stored electricity back to the grid under certain conditions to provide auxiliary services such as peak load regulation and frequency regulation. Therefore, determining the feasibility range of electric vehicles participating in V2G operations at different times and states is crucial to optimizing their interaction with the grid. For example, researchers may analyze factors such as the state of charge (SOC) of electric vehicles, user travel needs, and grid load conditions to determine under what conditions electric vehicles can participate in V2G operations without affecting user experience or grid stability. This is actually defining the "feasible domain" for electric vehicles to participate in V2G operations.

[0190] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0191] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0192] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0193] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0194] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0195] The modules described as separate components may or may not be physically separate, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed across multiple network modules. Some or all of the modules may be selected to achieve the purpose of this embodiment according to actual needs.

[0196] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0197] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0198] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0199] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An electric vehicle energy storage charging and discharging optimization scheduling method based on the V2G feasible region is characterized by: The steps include: Obtain the real-time braking signal of the regenerative braking system and the remaining power data of the battery management system, and analyze whether the initial determination conditions of the feasible domain are met based on the multi-source prediction algorithm, specifically whether the necessary conditions for energy flow interaction are met; When the initial conditions for the feasible region are met, the axle braking distribution characteristics are analyzed through vehicle dynamics modeling and stochastic differential equations to evaluate the dynamic changes in energy distribution during vehicle braking. The dynamic characteristics of the power grid are analyzed through grid load data modeling to identify the real-time characteristic curves of vehicle discharge and grid adaptation. Based on the dynamic characteristics of energy distribution during vehicle braking and the real-time characteristic curve of vehicle discharge and grid adaptation, the energy flow identification results when the vehicle is undergoing regenerative braking and grid discharge in parallel are determined; The energy flow identification results are used to characterize the coordinated state of regenerative braking energy and grid discharge power under the current working conditions; When the energy flow identification result is within the preset coupling range, the feasible region constraints that meet the vehicle driving safety and grid power requirements are determined; The preset coupling interval is used to characterize the coordinated state range of regenerative braking energy and grid discharge power; Based on the constraints of the feasible region and the dynamic change characteristics of energy distribution during vehicle braking, the output power of the on-board drive system and bidirectional inverter is dispatched, and the recovery power generated by regenerative braking and the grid discharge power are dynamically allocated.

2. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 1 is characterized in that: Obtain the real-time braking signal of the regenerative braking system and the remaining power data of the battery management system, and analyze whether the initial determination conditions of the feasible domain are met based on the multi-source prediction algorithm, including: Acquiring real-time braking signals from the regenerative braking system, including collecting real-time data of brake pressure and wheel speed from brake pressure sensors and wheel speed sensors; Obtaining the remaining power data of the battery management system, including obtaining the remaining power information and battery health status information of the vehicle battery through the battery management system; A multi-source prediction algorithm is used to integrate and analyze real-time braking signals, remaining power information, and battery health status information to determine whether the initial conditions of the feasible domain for interaction between the vehicle and the power grid are met.

3. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 1 is characterized in that: When the initial conditions for the feasible region are met, the axle braking distribution characteristics are analyzed through vehicle dynamics modeling and stochastic differential equations to evaluate the dynamic change characteristics of energy distribution during vehicle braking, including: Based on the vehicle's real-time driving status information, wheel speed, brake pressure and vehicle posture data are collected. Vehicle posture data includes the vehicle's pitch angle and roll angle; The wheel speed, brake pressure and vehicle posture data are calculated using the vehicle dynamics model to obtain the preliminary distribution of wheel axle braking energy; A stochastic differential equation model is used to dynamically optimize the initial distribution of axle braking energy, eliminating the interference of road friction coefficient changes and braking unevenness on energy distribution. The dynamic coefficient of brake energy distribution is calculated to quantify the dynamic change characteristics of energy distribution between axles during vehicle braking.

4. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 3 is characterized in that: The dynamic coefficient of brake energy distribution is calculated to quantify the dynamic characteristics of energy distribution between axles during vehicle braking. Specifically, Calculate the dynamic coefficient of brake energy distribution: ;in, Indicates the dynamic coefficient of braking energy distribution, Indicates the front wheel braking force, Represents the rear wheel braking force, and are the weight factors for the front and rear wheels respectively, and and are greater than 0, Indicates the difference in friction coefficient between the front and rear wheels.

5. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 3 is characterized in that: When the initial conditions for the feasible region are met, the grid dynamic characteristics are analyzed through grid load data modeling to identify the real-time characteristic curve of vehicle discharge and grid adaptation, including: Collect grid load data, including local grid voltage, frequency change rate, and load fluctuation characteristics; Preprocess the collected grid load data, including normalization, noise filtering, and data alignment, to eliminate the interference of different sampling frequencies and noise on the analysis results; A dynamic change model of the power grid load is constructed based on the time series model. The time series model uses historical data and real-time data of the power grid load to obtain the dynamic characteristics of the power grid load through fitting; The grid load dynamic change model and vehicle discharge parameters are used to analyze the real-time adaptability of the grid load to vehicle discharge, and the real-time characteristic curve of vehicle discharge and grid adaptation is output.

6. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 5 is characterized in that: A dynamic change model of the power grid load is constructed based on the time series model. The time series model uses historical data and real-time data of the power grid load to obtain the dynamic characteristics of the power grid load through fitting, specifically: The autoregressive integral moving average model is used to fit the grid load data. The formula is: ;in, Indicates time The grid load value, represents the autoregressive coefficient, Indicates time The error term, represents the moving average coefficient, and denote the autoregressive order and the moving average order respectively; The time series model uses historical data to predict the dynamic changes of load and provide the dynamic characteristics of power grid load.

7. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 5 is characterized in that: The real-time adaptability of the grid load to vehicle discharge is analyzed using the grid load dynamic change model and vehicle discharge parameters, and the real-time characteristic curve of vehicle discharge and grid adaptation is output, specifically: Conduct adaptability analysis on the dynamic change data of grid load generated by time series model and vehicle discharge parameters; Analyze the real-time adaptability of the grid load to vehicle discharge and calculate the adaptability index: ;in, represents the adaptability index, Indicates the vehicle discharge power and grid load power in the first The difference between the sampling points, Indicates the The load priority weight of each sampling point, Indicates the total number of load data sampling points within the sampling period; The adaptability index generated by the adaptability analysis generates a real-time characteristic curve of vehicle discharge and grid adaptation.

8. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 7 is characterized in that: Based on the dynamic characteristics of energy distribution during vehicle braking and the real-time characteristic curve of vehicle discharge and grid adaptation, the energy flow identification results when the vehicle is undergoing regenerative braking and grid discharge in parallel are determined, specifically including: The dynamic coefficient of brake energy distribution and the adaptability index are input into the energy flow identification model. The energy flow identification model is based on the threshold judgment rule and is calculated according to the following rules: When the dynamic coefficient of the braking energy distribution is greater than the corresponding preset threshold and the adaptability index is greater than the corresponding preset threshold, the energy flow identification result is a coordinated state; When the dynamic coefficient of braking energy distribution is greater than the corresponding preset threshold but the adaptability index is less than or equal to the corresponding preset threshold, the energy flow identification result is regenerative braking priority; When the dynamic coefficient of braking energy distribution is less than or equal to the corresponding preset threshold and the adaptability index is greater than the corresponding preset threshold, the energy flow identification result is grid discharge priority; When the dynamic coefficient of the braking energy distribution and the adaptability index are both less than or equal to the corresponding preset thresholds, the energy flow identification result is a non-cooperative state; Output the energy flow identification result according to the calculation result of the energy flow identification model.

9. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 1 is characterized in that: When the energy flow identification result is within the preset coupling range, the feasible region constraints that meet vehicle driving safety and grid power requirements are determined, including: Determine whether the energy flow identification result is within the preset coupling range; When the energy flow identification result is within the preset coupling interval, the vehicle driving state parameters are obtained, including vehicle speed, vehicle attitude angle and wheel slip rate, which are used to describe the dynamic safety state of the vehicle; Obtain grid power status parameters, including real-time grid voltage, frequency change rate, and load fluctuation characteristics, to describe the dynamic characteristics of grid power demand; Based on the vehicle driving state parameters and the grid power state parameters, the feasible region constraints are calculated through a multi-objective optimization algorithm. The multi-objective optimization algorithm uses the vehicle driving safety threshold and the grid power demand threshold as constraints to dynamically adjust the energy flow distribution ratio. Output feasible region constraints to limit the distribution range of vehicle regenerative braking energy recovery and grid discharge power.

10. The method for optimizing the charging and discharging of electric vehicle energy storage based on the V2G feasible region according to claim 3 is characterized in that: Based on the constraints of the feasible region and the dynamic characteristics of energy distribution during vehicle braking, the output power of the onboard drive system and bidirectional inverter is dispatched, and the recovery power generated by regenerative braking and the power discharged from the grid are dynamically allocated. Specifically, the following are performed: According to the feasible region constraints and the dynamic coefficient of braking energy distribution, the maximum allocation ratio of the recovery power generated by regenerative braking and the maximum allocation ratio of the grid discharge power are calculated; Based on the calculation results, the regenerative braking recovery power provided by the vehicle drive system and the grid discharge power output by the bidirectional inverter are determined respectively; The calculation results of regenerative braking recovery power and grid discharge power are transmitted as dynamic allocation instructions to the vehicle drive system and bidirectional inverter to adjust their actual output power and ensure that power is dynamically distributed in proportion.

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